Spontaneous preterm birth (SPB) is affected by various environmental exposures. However, there is still an urgent need to efficiently integrate exposomic information to build its prediction model and unveil the potential toxic pathways. Here, we conducted a nested case-control study by recruiting 30 women with SPB delivery (cases) and 30 women without (controls) at their early pregnancy. We analyzed various biomarkers of external chemical exposure, lipidomics, and immunity, resulting in 1081 exposure features. A logistic regression model (LR) was used to screen potential risk factors, and four statistical learners were used to establish SPB prediction models. Overall, the serum lipid concentration in cases was higher than in controls, while this was not the case for chemical and immune biomarkers. Random forest (RF) and extreme gradient boosting (XGboost) models had a relatively higher prediction accuracy of > 90%. Glycerophospholipids (GP) were the most abundant lipidomic features screened by LR, RF, and XGboost models, followed by glycerolipids and sphingolipids, shown as well as by enrichment analysis. Moreover, FA(21:0) had the largest contribution to the prediction performance. Maternal exposure to various elements can contribute to SPB risk due to their interaction with GP metabolism. Therefore, it is promising to use exposomic data to predict SPB risk and screen key molecular events.
Exposome has become the hotspot of next-generation health studies. To date, there is no available effective platform to standardize the analysis of exposomic data. In this study, we aim to propose one new framework of exposomic analysis and build up one novel integrated platform “ExposomeX” to expediate the discovery of the “Exposure-Biology-Disease” nexus. We have developed 13 standardized modules to accomplish six major functions including statistical learning (E-STAT), exposome database search (E-DB), mass spectrometry data processing (E-MS), meta-analysis (E-META), biological link via pathway integration and protein-protein interaction (E-BIO) and data visualization (E-VIZ). Using ExposomeX, we can effectively analyze the multiple-dimensional exposomics data and investigate the “Exposure-Biology-Disease” nexus by exploring mediation and interaction effects, understanding statistical and biological mechanisms, strengthening prediction performance, and automatically conducting meta-analysis based on well-established literature databases. The performance of ExposomeX has been well validated by re-analyzing two previous multi-omics studies. Additionally, ExposomeX can efficiently help discover new associations, as well as relevant in-depth biological pathways via protein-protein interaction and gene ontology network analysis. In sum, we have proposed a novel framework for standardized exposomic analysis, which can be accessed using both R and online interactive platform (http://www.exposomex.cn/).
Exposome has become the hotspot of next-generation health studies. To date, there is no available effective platform to standardize the analysis of exposomic data. In this study, we aim to propose one new framework of exposomic analysis and build up one novel integrated platform "ExposomeX" to expediate the discovery of the "Exposure-Biology-Disease" nexus. We have developed 13 standardized modules to accomplish six major functions including statistical learning (E-STAT), exposome database search (E-DB), mass spectrometry data processing (E-MS), meta-analysis (E-META), biological link via pathway integration and protein-protein interaction (E-BIO) and data visualization (E-VIZ). Using ExposomeX, we can effectively analyze the multiple-dimensional exposomics data and investigate the "Exposure-Biology-Disease" nexus by exploring mediation and interaction effects, understanding statistical and biological mechanisms, strengthening prediction performance, and automatically conducting meta-analysis based on well-established literature databases. The performance of ExposomeX has been well validated by re-analyzing two previous multi-omics studies. Additionally, ExposomeX can efficiently help discover new associations, as well as relevant in-depth biological pathways via protein-protein interaction and gene ontology network analysis. In sum, we have proposed a novel framework for standardized exposomic analysis, which can be accessed using both R and online interactive platform (http://www.exposomex.cn/).
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